outward fdi and domestic input distortions: evidence …mjyu.ccer.pku.edu.cn/docs/ej_cty.pdf · *...

The Economic Journal, 1–33 DOI: 10.1093/ej/uez034 C© 2018 Royal Economic Society. Published by Oxford University Press. All rights reserved. Forpermissions please contact [email protected].

Advance Access Publication Date: 2 July 2019

OUTWARD FDI AND DOMESTIC INPUT DISTORTIONS:

EVIDENCE FROM CHINESE FIRMS*

Cheng Chen, Wei Tian and Miaojie Yu

We examine how domestic distortions affect firms’ production strategies abroad by documenting two puz-zling findings using Chinese firm-level data of manufacturing firms. First, private multinational corporations(MNCs) are less productive than state-owned MNCs, but they are more productive than state-owned enter-prises overall. Second, there are disproportionately fewer state-owned MNCs than private MNCs. We build amodel to rationalise these findings by showing that discrimination against private firms domestically incen-tivises them to produce abroad. The model shows that selection reversal is more pronounced in industrieswith more severe discrimination against private firms, which receives empirical support.

1. Motivation and Findings

Foreign direct investment (FDI) and the emergence of multinational corporations (MNCs) aredominant features of the world economy.1 Therefore, understanding the behaviour of MNCs andpatterns of FDI is important for the analysis of the aggregate productivity and resource allocation.

The sharp increase in outward FDI from developing countries in the past decade has beenphenomenal, and this is especially true for China. The United Nations Conference on Trade andDevelopment (UNCTAD) World Investment Report (UNCTAD, 2015) shows that outward FDIflows from developing economies have already accounted for more than 33% of overall FDIflows, up from 13% in 2007. Furthermore, despite the fact that global FDI flows plummetedby 16% in 2014, MNCs from developing economies invested almost US$468 billion abroad in2014, an increase of 23% over the previous year.2 As the largest developing country in the world,China has seen an astonishing increase in its outward FDI flows in the past decade. In 2015,China’s outward FDI reached the level of 9.9% of the world’s total FDI flows, which made Chinathe second-largest home country of FDI outflows globally. In addition, manufacturing outwardFDI from China is becoming more important in China’s total outward FDI flows. Its share in

* Corresponding author: Miaojie Yu, China Center for Economic Research (CCER), National School of Development,Peking University, Beijing 100871, China. Email: [email protected]

This paper was accepted on 13 August 2018. The Editor was Morten Ravn.

We thank the editor, two referees, James Anderson, Pol Antràs, Sam Bazzi, Svetlana Demidova, Hanming Fang, RobFeenstra, Gordon Hanson, Chang-Tai Hsieh, Yi Huang, Hong Ma, Kalina Manova, Marc Melitz, Nina Pavcnik, LarryQiu, Danyang Shen, Michael Song, Chang Sun, Hei-Wai Tang, Zhigang Tao, Stephen Terry, Daniel Trefler, Shang-JinWei, Daniel Xu, Stephen Yeaple and Yifan Zhang for their insightful comments. We thank seminar participants atvarious institutions for their very helpful suggestions and comments. Cheng Chen thanks IED of Boston University fortheir hospitality and the Hong Kong government for financial support (HKGRF project code: 17500618). Wei Tian andMiaojie Yu thank China’s natural (social) science foundation for funding support (No. 71503043, 71573006, 71625007,16AZD003). They thank the Histotsubashi Institute of Advanced Studies for hospitality when they were writing thearticle. However, all remaining errors are the authors’.

1 MNCs refer to firms that own or control production of goods or services in countries other than their home country.FDI includes mergers and acquisitions, building new facilities, reinvesting profits earned from overseas operations andintra-company loans.

2 The UNCTAD World Investment Report also demonstrates that FDI stock from developing economies to otherdeveloping economies grew by two-thirds, from US$1.7 trillion in 2009 to US$2.9 trillion in 2013. It also reports thattransition economies now represent nine of the 20 largest investor economies globally (UNCTAD, 2015).

[ 1 ]

mailto:[email protected]:[email protected]

2 the economic journal

China’s total outward FDI has increased from 9.9% in 2012 to 18.3% in 2016.3 In sum, patternsof China’s manufacturing outward FDI flows need to be explored, given its importance for theworld economy.

This study investigates the patterns of China’s outward FDI of manufacturing firms throughthe lens of domestic input market distortions. It has been documented that discrimination againstprivate firms is a fundamental issue for the Chinese economy. For instance, state-owned enter-prises (SOEs) enjoy preferential access to financing from state-owned banks (Dollar and Wei,2007; Song et al., 2011; Khandelwal et al., 2013; Manova et al., 2015). Moreover, Khandelwalet al. (2013), and Bai et al. (2017) document that private firms had been treated unequally bythe Chinese government in the exporting market. In short, it is natural to link the behaviour ofChinese MNCs to domestic distortions in China.

To the best of our knowledge, little work has studied how institutional distortions at homeaffect firms’ investment patterns abroad. The reason is that developed economies have been thehome countries of outward FDI for many decades, and their economies are much less likely tobe subject to distortions compared with developing economies. By contrast, various distortionsare fundamental features of developing countries. For instance, size-dependent policies and redtape have been shown to generate substantial impacts on firm growth and resource allocation inIndia (Hsieh and Klenow, 2009; 2014). The government discriminates against private firms inChina (Huang, 2003; 2008; Brandt et al., 2013). Moreover, there is already anecdotal evidencedocumenting how private firms in China circumvent these distortions by doing business abroad.For instance, the key to the success of the Geely automobile company (a private car maker inChina) was to expand internationally even at early stages of its development (e.g., the purchaseof Volvo in 2010). Thus, distortions in the domestic market do seem to affect firms’ decisionsconcerning going abroad.

We document three stylised facts of China’s MNCs in manufacturing sectors to motivateour theory. First, although private non-MNCs are more productive than state-owned non-MNCson average, private MNCs are actually less productive than state-owned MNCs on average.Moreover, when looking at the productivity distribution of state-owned MNCs and private MNCs,we find that at each percentile, state-owned MNCs have higher (normalised) TFP compared withprivate MNCs. Second, compared with private firms, the fraction of firms that undertake outwardFDI is smaller among SOEs. Finally, the relative size of MNCs (i.e., average size of MNCsdivided by average size of non-exporting firms) is smaller among private firms than amongSOEs.

These findings are counterintuitive. First, SOEs are much larger than private firms in China,and larger firms are more likely to become MNCs. Furthermore, it has been documented thatSOEs receive substantial support from the Chinese government for investing abroad. Thus, whydid so few SOEs actually invest abroad? Finally, it has been documented that SOEs are lessproductive than private firms in China (e.g., Brandt et al., 2012; Khandelwal et al., 2013). Ourdata also show this pattern when we look at non-exporting and exporting (but non-multinational)firms. Why is this pattern reversed when we focus on MNCs?

To rationalise these puzzling findings, we build a model based on Helpman et al. (2004; hence-forth, HMY) and highlight two economic forces: institutional arbitrage and selection reversal.Two key departures we make from HMY are the addition of capital (or land) used in the produc-tion process and asymmetric distortions across borders. Specifically, we assume that private firms

3 See Statistical Bulletin of China’s Outward Foreign Direct Investment (2015 and 2016).

C© 2019 Royal Economic Society.

3

pay a higher capital rental price (and land price) when producing domestically (compared withSOEs), while all firms pay the same input prices when they produce abroad. The existence of theinput price wedge comes from the capital and land markets, as the banking sector is dominatedby state-owned banks and land is largely owned by the government in China. In our data, privatefirms pay higher interest rates and unit land price than SOEs, which is equivalent to an implicitinput tax levied on private firms. When firms produce abroad, at least a part of the input pricewedge ceases to exist, as the capital and land markets in foreign economies are not controlled bythe Chinese government. In other words, the domestic input price (relative to the foreign inputprice) private firms face is higher than that of SOEs.4

As a result of this asymmetry, there is an extra incentive for private firms to produce abroad,since they can circumvent the input market distortion that exists only domestically by becomingMNCs (i.e., institutional arbitrage). Absent the domestic distortion, there would be no differencein the selection into the (domestic and) FDI market, since SOEs and private firms face the samedomestic (and foreign) market environment. When there is a domestic distortion, selection intothe domestic market is tougher for private firms. However, since they receive an extra benefit fromproducing abroad (i.e., not just the saving on the variable trade cost), the incentive of becomingan MNC is higher for them. This leads to less tough selection into the FDI market for privatefirms, which is termed ’selection reversal’ in this article. This reversal rationalises why there aredisproportionately fewer MNCs among SOEs than among private firms and why private MNCsare less productive than state-owned MNCs.

In addition to explaining the stylised facts, our model yields several additional empiricalpredictions. First, conditional on other firm-level characteristics, a private firm sells dispropor-tionately more in the foreign market because of the non-existence of distortion abroad. Second,as the distortion exists in the capital and land markets (rather than in the labour market), theselection reversal for state-owned MNCs is more pronounced in capital intensive industries andin industries in which the (industry-level) interest rate (or land price) differential between privatefirms and SOEs is larger. We present supporting evidence for these additional predictions.

It might be true that Chinese firms can borrow money from domestic banks to finance a partof their outward FDI projects. Thus, the discrimination against private firms in the credit marketmight still exist even when private firms invest abroad. However, even if this is true for a fractionof firms in our study, the discrimination against private firms in the land market is limited tothe domestic market as firms cannot move land abroad to do investment. Importantly, we findevidence that the selection reversal is more pronounced in industries in which the unit land pricedifferential (between private firms and SOEs) is larger. Therefore, the asymmetric distortion inthe land market across border plays a role in affecting Chinese firms’ FDI decisions.

The data set of outward FDI used in this article is a representative sample of China’s outwardFDI projects, as the ministry of commerce of China requires all outward FDI deals whoseinvestment amounts are higher than US$10 million to be reported to the ministry. Admittedly,our data set loses small outward FDI projects which are most likely to be conducted by privatefirms.5 Naturally, the exclusion of small private outward FDI deals would prevent us from finding

4 It is plausible that the distortion in the input market shows up as a subsidy to SOEs. In this scenario, SOEs have lessof an incentive to undertake FDI, since the relative domestic input price they face is lower, which is the same as in ourmain model. This situation results in tougher selection into the FDI market for SOEs as well, which leads to the sameempirical predictions.

5 Shen (2013) and Chen et al. (2016) draw the same conclusion that outward FDI projects conducted by private firmsare substantially underreported in the data set provided by the ministry of commerce.



the selection reversal. Given that we do find the selection reversal, this pattern should be morepronounced if we used an universal data set which includes small outward FDI deals.

It is important to stress that Chinese firms have different motives to undertake outward FDI. Inthis article, we focus on manufacturing FDI and exclude outward FDI projects in the constructionand mining sectors for two reasons. First, manufacturing firms’ investment behaviour is more re-lated to firm performance and profit-driven.6 Second, the canonical model of FDI (i.e., HMY) andasymmetric distortions across border fit well into the case of manufacturing MNCs from China.In particular, the share of manufacturing FDI in total outward FDI is much larger within China’sinvestment into developed economies than within its investment into developing economies.7 Asdeveloped economies probably have fewer distortions than developing economies, our story fitsbetter into manufacturing MNCs from China.

We also use several sub-samples of our data sets to exclude alternative hypotheses for theselection reversal pattern. First, we find that the selection reversal does not hold when we compareprivate exporting firms to state-owned exporting firms. Given that exporting does not allow privatefirms to escape from domestic input distortions, this finding excludes an alternative hypothesisrelated to discriminations in the output market. Second, we find that the selection reversal patternstill exists, even after we exclude merger and acquisition (M&A)-type FDI projects or FDI projectsto tax heaven economies from our analysis. As the motive of acquiring better technologies andbrands is more pronounced for M&A-type FDI (compared with greenfield-type FDI) and themotive of shifting profits is more pronounced for FDI into tax heaven economies, these two typesof motives cannot explain our empirical finding of selection reversal.

Although we focus on how a particular type of asymmetric institutional treatment affectseconomic outcomes, the insights of this study apply to other circumstances as well. For instance,a rising number of talented and wealthy French people moved abroad because of the increasingtax rates in France.8 This serves as a perfect example of institutional arbitrage. In India, red tapehas forced many talented entrepreneurs to leave the country and start their businesses abroad aswell.9

This study aims to speak to the literature on FDI and MNCs. In research on vertical FDI,Helpman (1984) insightfully points out how the difference in factor prices across countries affectspatterns of vertical FDI. Antràs (2003; 2005) and Antràs and Helpman (2004) emphasise theimportance of contractual frictions for shaping the pattern of FDI and outsourcing. In researchon horizontal FDI, Markusen (1984) postulates the concentration-proximity trade-off, whichreceives empirical support from Brainard (1997). More recently, HMY (2004) develop a modelof trade and FDI with heterogeneous firms. Our study contributes to this literature by pointingout another motive for firms to engage in FDI.

This study is also related to the literature that substantiates the existence of resource misal-location in developing economies. Hsieh and Klenow’s (2009) pioneering work substantiatessubstantial resource misallocation in China and India. Midrigan and Xu (2014), Moll (2014),and Gopinath et al. (2017) study the aggregate impact of financial frictions on firm productivityand investment. Guner et al. (2008), Restuccia and Rogerson (2008), and Garicano et al. (2016)

6 Shen (2013) and Chen et al. (2016) find empirical support for this argument.7 According to Statistical Bulletin of China’s Outward Foreign Direct Investment (2015), the share of manufacturing

FDI in total outward FDI is 26.3% for China’s investment into the United States and 19.7% for China’s investment intothe EU. Note that the average share of manufacturing FDI in total outward FDI is 13.7% across all countries.

8 See http://www.france24.com/en/20150808-france-wealthy-flee-high-taxes-les-echos-figures.9 Readers interested in studying anecdotal evidence of this can find it at http://www.thehindu.com/news/national/red

-tape-forces-top-indian-entrepreneurs-to-shift-overseas/article7367731.ece.


http://www.france24.com/en/20150808-france-wealthy-flee-high-taxes-les-echos-figureshttp://www.thehindu.com/news/national/red-tape-forces-top-indian-entrepreneurs-to-shift-overseas/article7367731.ece

5

explore the impact of size-dependent policies on aggregate productivity.10 Our work contributesto this research area by showing a link between domestic distortions and firms’ behaviour in theglobal market.

The third related literature is the research on distortions in China and FDI decisions of Chinesefirms (Brandt et al. 2013; Bai et al., 2015). Using a similar data set to ours, Shen (2013) and Chenet al. (2016) study the motives and consequences of China’s outward FDI into Africa. Using thesame data set, Tian and Yu (2015) document the sorting pattern of Chinese MNCs, but abstractaway from the difference between state-owned MNCs and private MNCs. Compared with theexisting work, our article links firms’ outward FDI decisions to domestic distortions.

2. Data and Stylised Facts

2.1. Data

Four main data sets are used in the present article, which we introduce as follows. Detaileddiscussions of the four data sets can be found in Online Appendix A.

Annual Survey of Industrial Firms Data. Our first data set is a production data set of Chinesemanufacturing firms from 2000 to 2013, which comes from the Annual Survey of IndustrialFirms (ASIF) complied by the National Bureau of Statistics (NBS) of China. All SOEs and‘above-scale’ non-SOEs (i.e., private firms) are included in the data set.11

FDI Decision Data. The nationwide data set of Chinese firms’ FDI decisions was obtainedfrom the Ministry of Commerce of China (MOC). MOC requires every Chinese MNC to reportits investment activity abroad since 1980, if it is above US$10 million. To invest abroad, everyChinese firm is required by the government to apply to the MOC for approval, or for registrationif no approval is needed.12 In addition, the nationwide FDI decision data report FDI starters byyear.

Firm Land Price Data. To explicitly show the price discrimination against private firms ininput factor markets, we use a comprehensive and novel firm-level data set of land price, whichis collected from the official website of China’s land transaction monitoring system operated andmaintained by the Ministry of Land and Resources. This monitoring system contains detailedinformation of land transactions, including land area, deal price, assigner and assignee.

Orbis Data. Finally, we use the Orbis data from Bureau Van Dijk from 2005 to 2014, sincethey contain detailed financial information on foreign affiliates of Chinese MNCs. For the databefore 2011, we merge our ASIF data with the Orbis data by matching the names in Chinese. Forthe data after 2011, we merge our ASIF data with the Orbis data using (Chinese) parent firms’trade registration number which is contained in both data sets after 2011. We use the merged dataset to study how Chinese MNCs allocate their sales across border.

Data Merge. We merge the firm-level FDI and land price data sets with the manufacturing pro-duction database. Although the three data sets share a common variable—the firm’s identificationnumber—their coding systems are completely different. Hence, we use alternative methods tomerge the three data sets. The matching procedure involves three steps. First, we match the three

10 For a synthesis of work on misallocation and distortion, see Restuccia and Rogerson (2013).11 The ‘above-scale’ firms are defined as firms with annual sales of RMB5 million (or equivalently, about US$830,000)

or more before 2010 and with RMB10 million afterward.12 Note that the SOEs directly controlled by central government are also required to report their FDI deals. This is

why our data samples include such firms as CNPC (China National Petroleum Corporation), CPCC (China PetroleumChemical Corporation), and China FResource Corporation.



Table 1. FDI Share in Chinese Manufacturing Firms (2000–12).

Firm type 2000 2002 2004 2006 2008 2010 2012

(1) Manufacturing firms 83,579 110,498 199,873 194,201 158,220 306,366 283,018(2) FDI mfg. parent firms (inour sample)

5 9 56 562 867 1945 5501

(3) FDI share (%) 0.01 0.01 0.03 0.29 0.55 0.64 1.94(4) FDI mfg. parent firms (inthe bulletin)

– – – 2670 3650 4654 6744

(5) Matching percentage (%) – – – 21.1 23.8 41.8 81.6(6) FDI mfg. SOEs share (%,in our sample)

20.0 22.2 5.35 3.02 1.49 1.23 1.81

(7) FDI SOEs share (%, in thebulletin)

– – – 26.0 16.1 10.2 9.1

Notes: Data on China’s MNCs were obtained from the Ministry of Commerce of China. FDI share in row (3) is obtainedby dividing the number of FDI manufacturing firms in row (2) by the number of manufacturing firms in row (1). That is,(3) = (2)/(1). Matching percentage in row (5) equals the number of FDI manufacturing parent firms in our sample dividedby number of FDI manufacturing parent firms in the bulletin (i.e., [5] = (2] / [4]). Numbers of FDI manufacturing parentfirms in the bulletin before 2006 in column (4) are unavailable. FDI manufacturing SOEs share in row (6) reports thepercentage share of state-owned manufacturing MNCs among all manufacturing MNCs in our sample. FDI SOEs sharein row (7) denotes the percentage share of state-owned MNCs among all MNCs in the bulletin.

data sets by using each firm’s Chinese name and year. If a firm has an exact Chinese name in allthree data sets in a particular year, it is considered an identical firm. Still, this method could misssome firms since the Chinese name for an identical company may not have the exact Chinesecharacters in the three data sets, although they share some common strings. Our second step is todecompose a firm name into several strings referring to its location, industry, business type, andspecific name. If a company has all identical strings in the three data sets, such a firm is classifiedas an identical firm. Finally, all approximate string-matching procedures are double-checkedmanually.

We show the matching quality of our data in Table 1, and detailed discussions can be found inOnline Appendix A. In short, we are able to match 21−42% of manufacturing MNCs reportedin the statistical bulletin to our ASIF data between 2006 and 2010. Furthermore, the matchingquality has improved substantially afterwards. In addition, our matched sample exhibits the sametrend as in the statistical bulletin: The proportion of state-owned MNCs is decreasing over years.

Although our firm-level data set covers 2000–13, we use data for 2000–08 to conduct ourmain empirical analysis, as the data after 2008 lack information on (parent) firm’s value-addedand use of materials, which disenables us to estimate firm productivity (a key variable in ourempirical analysis). We instead use data after 2008 for robustness checks in Online Appendix.As highlighted by Feenstra, Li and Yu (2014), some observations in this firm-level productiondata set are noisy and misleading, largely because of misreporting by some firms. To guaranteethat our estimation sample is reliable and accurate, we screen the sample and omit outliers byadopting the criteria a là Feenstra et al. (2014).13

2.2. Measures

The SOE indicator and measured firm productivity are the two key variables used in this article.This subsection describes how we construct these two measures.

13 For details, see Online Appendix A.


7

2.2.1. SOE measuresWe define SOEs using two methods. The first one is to adopt the official definition of SOEs,as reported in the China City Statistical Yearbook (2006), by using information on firms’ legalregistration. A firm is classified as an SOE if its legal registration identification number belongsto the following categories: state-owned sole enterprises, state-owned joint venture enterprisesand state-owned and collective joint venture enterprises. State-owned limited corporations areexcluded from SOEs by this measure. As this is the conventional measure widely used in theliterature, we thus adopt such a measure as the default measure to conduct our empirical analysis.Table 1 of the Online Appendix provides summary statistics for the SOE dummy used in thisstudy.14

Recently, Hsieh and Song (2015) introduce a broader definition of SOEs and suggest defininga firm as an SOE when its state-owned equity share is greater than or equal to 50%. Along thisline, we introduce an alternative way to define SOEs following their suggestion. As a result, afirm is defined as an SOE if either (i) it is classified as an SOE using the conventional measure;or (ii) its state-owned equity share is greater than or equal to 50%. We use such a broadly definedSOE dummy in our robustness checks.

2.2.2. TFP measuresFirst and foremost, we estimate firm TFP using the augmented Olley–Pakes (1996) approach asadopted in Yu (2015). Compared with the standard Olley–Pakes (1996) approach, our approachhas five new elements. First, we estimate the production function for MNCs and non-MNCsin each industry separately, since these two types of firms may adopt different technology.15

Second, we use detailed industry-level input and output prices to deflate a firm’s input use andrevenue in our productivity estimation. As the revenue-based TFP may pick up differences inprice-cost markup and prices across firms (De Loecker and Warzynski, 2012), an ideal methodis to use firm-specific price deflators to construct quantity-based TFP. However, such data arenot available in China. To mitigate this problem, we follow Brandt et al. (2012) to use four-digitChinese Industrial Classification (CIC)-level input and output prices to deflate firm’s input useand revenue. Once industry-level price deflators are well defined and the price-cost markup ispositively associated with true efficiency, revenue-based TFP captures the true efficiency of thefirm reasonably well (Bernard et al., 2003).

Third, we take the effect of China’s accession to the WTO (on firm performance) into account,as Chinese firms may export more or do more outward FDI due to the expansion of foreignmarkets after 2001. We thus include a WTO dummy in the inversion step of our productivityestimation. Fourth, and similarly, we also include a processing export dummy in the inversionstep as processing exporters and non-processing firms may use different technology (Feenstra andHanson, 2005). Last and most important, we also add an SOE indicator and an export indicatorinto the control function in the first-step Olley–Pakes estimates. In particular, we include the SOEindicator (and the export indicator) and its interaction terms with log-capital and log-investmentto approximate the fourth-order polynomials in the inversion step of the TFP estimates.

As stressed in Arkolakis (2010), firm TFP cannot be directly comparable across industries. Wethus calculate the relative TFP (RTFP) by normalising our augmented Olley–Pakes TFP in each

14 For details, see Online Appendix A.15 As a robustness check, we also pool MNCs and non-MNCs together and, in the inversion step of the productivity

estimation, re-estimate the production function by including a dummy variable for MNC status. The results generated bythis alternative method do not change our subsequent empirical findings, as shown by Table 2 in the Online Appendix.



industry. As suggested by Ghandi et al. (2016), the correct identification of the flexible inputelasticities should be based on the estimation of gross output production functions. Thus, all theTFP measures are conducted by using gross output production function.16

Although we control for the SOE indicator in the productivity estimation described above,it might still be unclear whether the TFP difference between SOE and private firms is causedby input factor distortions (or any other factors). If input factor distortions play an essentialrole in determining firms’ input use, it should be observed that SOEs are more capital intensiveeven within each narrowly defined industry (after controlling for firm size and other year-variantfactors), as SOEs can access working capital at lower cost. Inspired by this intuition, we firstregress the capital–labour ratio of the firm on its size (proxied by firm sales), industry fixed effects(at the finest four-digit CIC level), and year fixed effects, to obtain firm-level clustered residuals.We then interact these residuals with log-capital and log-investment as additional variables in thefourth-order polynomials used in the inversion step of the TFP estimates. We thus re-estimate ouraugmented relative TFP, taking into consideration the input distortions (RTFPDistort). Finally, wealso consider another specification (RT FP DistortSOE ) by including the firm-level clustered residualsand the SOE indicator (with interactions with log-capital and log investment) in the inversionstep of the TFP estimates for robustness checks.

2.3. Stylised Facts

The main purpose of this subsection is to document three stylised facts using the merged datasets. As our interest is to explore how resource misallocation (across firm type) at home affectsChinese firms’ outward FDI behaviour, we compare state-owned MNCs with private MNCs whenstating these stylised patterns.

2.3.1. Stylised fact one: Productivity premium for state-owned MNCsTable 2 reports the difference in our augmented Olley–Pakes TFP estimates between SOEsand private firms. Simple t-tests in columns (1) and (3) show that, among non-MNCs and non-exporting firms, private firms are more productive than SOEs. To confirm this finding, we performnearest-neighbour propensity score matching, by choosing a dummy variable for capital-intensiveindustries and the year as covariates.17 To avoid the case in which multiple observations havethe same propensity score, we perform a random sorting before matching. Columns (2) and (4)present the estimates for average treatment for the treated for private firms. Again, the coefficientsof the productivity difference between SOEs and private firms are highly significant, suggestingthat non-multinational (and non-exporting) SOEs are less productive than non-multinational (andnon-exporting) private firms. The findings for non-MNCs are consistent with other studies, suchas Hsieh and Song (2015).

By contrast, a selection reversal is found when we focus on MNCs only. That is, private MNCs(i.e., private parent firms) are on average less productive than state-owned MNCs (i.e., state-owned parent firms), which is shown in column (5) in Table 2. To confirm this finding, we focuson the productivity difference between private and state-owned MNCs that are engaged in FDI

16 We thank a referee for pointing this out.17 In Melitz-type models (Melitz, 2003; Helpman et al., 2004), firm size is a sufficient statistic for productivity. The

model we will present is an extension of Helpman et al. (2004). Therefore, we do not use firm sales or employment asour covariates in the propensity score matching.


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l-in

tens

ive

indu

stri

esan

dye

arar

eus

edas

cova

riat

esto

obta

inth

epr

open

sity

scor

efo

rnon

-MN

Cs

inco

lum

ns(2

)and

(4).

Col

umns

(5)–

(8)s

how

that

priv

ate

MN

Cs

are

less

prod

uctiv

eth

anst

ate-

owne

dM

NC

sw

ithal

lfir

ms

and

with

expo

rts

only

,res

pect

ivel

y.A

dum

my

vari

able

for

capi

tal-

inte

nsiv

ein

dust

ries

,adu

mm

yva

riab

lefo

rri

chde

stin

atio

nec

onom

ies,

outw

ard

FDIm

ode

and

year

are

used

asco

vari

ates

toob

tain

the

prop

ensi

tysc

ore

forM

NC

sin

colu

mns

(6)a

nd(8

).In

row

s(i

ii)an

d(i

v)pr

ivat

efir

ms

and

SOE

sar

ede

fined

byus

ing

the

stat

esh

are

infir

m’s

owne

rshi

pa

làH

sieh

and

Song

(201

5).T

henu

mbe

rsin

pare

nthe

ses

are

t-va

lues

.***

(**,*

)de

note

sth

esi

gnifi

canc

eat

1%(5

%,1

0%).

TFP

ism

easu

red

byth

eau

gmen

ted

Olle

y–Pa

kes

TFP

(see

text

for

deta

ils).



Table 3. Selection Reversal: Disproportionately More Private MNCs.

Category 2000–8 2000–13

# of # of Fraction of # of # of Fraction ofMNCs MNCs all firms MNCs MNCs all firms MNCs

(1) (2) (3) (4) (5) (6)

(i) Private firms 3,623 1,335,514 0.27% 21,426 2,287,915 0.94%(ii) SOE 104 40,612 0.25% 270 66,192 0.41%

Ownership defined by state share(iii) Private firms 3,622 1,097,322 0.33% 21,130 2,273,486 0.93%(iv) SOE 105 43,512 0.24% 566 80,621 0.70%

Notes: Column (3) reports the fraction of MNCs that is obtained by dividing column (1) by column (2) for years 2000–8.Similarly, column (6) reports the fraction of MNCs that is obtained by dividing column (4) by column (5) for years2000–13. Clearly, the share of MNCs is smaller among SOEs than among private firms, which is consistent with part 3of Proposition 1. In rows (iii) and (iv) private firms and SOEs are defined by using the state share in firm’s ownershipa là Hsieh and Song (2015). Note that we lose some observations when defining SOEs using the state share in firm’sownership, as some firms did not report their state shares in our data. Refer to the texts for details.

and exporting as well.18 Column (7) reveals the same pattern. In columns (6) and (8), we performnearest-neighbour propensity score matching by choosing a dummy variable for capital-intensiveindustries, a dummy variable for rich destination economies, outward FDI mode (i.e., horizontal,vertical or R&D seeking) and the year as covariates. The results reported in these two columnsconfirm our finding that private MNCs are on average less productive than state-owned MNCs,even after we have controlled for aggregate-level factors. For more details, see Online AppendixB.

The lower module of Table 2 presents evidence of the selection reversal using a broadlydefined SOE indicator à la Hsieh and Song (2015). Compared with the numbers of MNCs andSOEs shown in the upper module, there are more SOEs engaged in outward FDI and more firmsclassified as SOEs when we use the broadly defined SOE dummy.

In order to further validate our finding, we run simple OLS regressions. Specifically, we firstregress the estimated TFP on the SOE indicator, the interaction term between SOE indicator andMNC indicator, and the firm fixed effects. Detailed discissions can be found in Online AppendixA. In short, we find that the selection reversal holds in the regression results, as the own coefficientof the SOE indicator and its interaction term with MNC indicator are negatively (and positively)significant, respectively.

Table 3 reports number of MNCs by types of ownership and the consequent fraction of MNCsduring the sample year. There are 566 broadly defined state-owned MNCs in our sample between2000 and 2013, which double its counterpart when SOEs are measured in a conventional way.Still, the evidence shows that private MNCs are less productive than state-owned MNCs, althoughprivate non-MNCs are more productive than state-owned MNCs.

Our first stylised fact is robust to different TFP measures as shown in Table 4. Columns (1),(4) and (7) report relative TFP for all firms, non-MNCs and MNCs, respectively. A firm’s relativeTFP is obtained by scaling down firm TFP in each industry after normalising the TFP of themost productive firm in that industry to one (see Arkolakis, 2010; Groizard et al., 2015). Afternormalisation, we calculate the relative TFP of firms in each industry. The TFP measure used incolumns (2), (5) and (8), RT FP distort , takes firm’s input factor distortions into account when

18 In reality, some Chinese MNCs engage in outward FDI and exporting. This is especially true for firms that undertakedistribution FDI by setting up trade office abroad to promote exports. See Tian and Yu (2015) for detailed discussions.


11

Tabl

e4.

Pro

duct

ivit

yP

rem

ium

ofSt

ate-

Ow

ned

MN

Can

dR

elat

ive

TF

P(2

000–

8).

Cat

egor

yA

llfir

ms

Non

-MN

Cfir

ms

MN

Cfir

ms

Mea

sure

sof

RT

FPR

TF

PO

PR

TF

PD

isto

rtR

TF

PD

isto

rt

soe

RT

FP

OP

RT

FP

Dis

tort

RT

FP

Dis

tort

soe

RT

FP

OP

RT

FP

Dis

tort

RT

FP

Dis

tort

soe

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(i)

Priv

ate

firm

s0.

506

0.49

40.

497

0.50

50.

494

0.49

70.

616

0.50

00.

503

(ii)

SOE

0.41

20.

478

0.48

10.

411

0.47

90.

481

0.65

00.

528

0.53

2D

iffe

renc

e=

(i)–

(ii)

0.09

4***

0.01

6***

0.01

6***

0.09

4***

0.01

5***

0.01

6***

−0.

034*

−0.

028*

**−

0.02

9***

(93.

95)

(46.

42)

(46.

29)

(97.

07)

(46.

53)

(46.

40)

(−1.

69)

(−2.

69)

(−2.

73)

Cap

ital-

inte

nsiv

ein

dust

ries

only

(iii)

Priv

ate

firm

s0.

509

0.50

00.

503

0.50

90.

500

0.50

30.

624

0.50

50.

509

(iv)

SOE

0.42

20.

477

0.48

00.

422

0.47

70.

480

0.67

60.

525

0.52

9D

iffe

renc

e=

(iii)

–(v

)0.

087*

**0.

023*

**0.

023*

**0.

087*

**0.

023*

**0.

023*

**−

0.05

2**

−0.

020*

−0.

020*

(78.

03)

(59.

05)

(59.

54)

(78.

28)

(59.

14)

(59.

62)

(−2.

39)

(−1.

65)

(−1.

64)

Not

es:

Num

bers

inpa

rent

hese

sar

et-

valu

es.*

**(*

*,*

)de

note

sth

esi

gnifi

canc

eat

1(5,

10)%

,res

pect

ivel

y.C

olum

ns(1

)–(3

)sh

owth

atpr

ivat

efir

ms

have

high

erre

lativ

eT

FPth

anSO

Es

for

all

firm

s.Si

mila

rly,

colu

mns

(4)–

(6)

show

that

priv

ate

non-

MN

Cfir

ms

have

high

erre

lativ

eT

FPth

anSO

Eno

n-M

NC

firm

s.C

olum

ns(7

)–(9

)sh

owth

atpr

ivat

eM

NC

firm

sar

ele

sspr

oduc

tive

than

stat

e-ow

ned

MN

Cs.

Col

umns

(1),

(4)

and

(7)

are

rela

tive

Olle

y–Pa

kes

TFP

.C

olum

ns(2

),(5

)an

d(8

)ar

ere

lativ

eT

FPfe

atur

edw

ithin

put

fact

ordi

stor

tions

.C

olum

ns(3

),(6

)an

d(9

)ar

ere

lativ

eT

FPfe

atur

edw

ithin

put

fact

ordi

stor

tions

and

inte

ract

edSO

Edu

mm

yw

ithot

her

poly

nom

ials

.T

heup

per

mod

ule

incl

udes

all

sam

ple

indu

stri

es,w

here

asth

ebo

ttom

one

incl

udes

capi

tal-

inte

nsiv

ein

dust

ries

only

,whi

chac

coun

tfor

arou

ndth

ree-

quar

ters

ofth

een

tire

sam

ple.



we estimate a firm’s relative TFP. The alternative firm TFP measure, RT FP distortsoe , reported incolumns (3), (6) and (9), puts the SOE dummy, distortion residuals and their interaction termswith other firm-level key variables into TFP estimations, as discussed above. Again, our findingsare robust to the different TFP measures. Our data clearly exhibit selection reversal in the sensethat private MNCs are less productive than state-owned MNCs.

Equally interestingly, we then look at the productivity difference between state-owned andprivate MNCs industry by industry. To do so, we separate all industries into two categories:capital-intensive and labour-intensive, according to the official definition adopted by the NationalStatistical Bureau of China.19 The lower module of Table 4 shows that a productivity premiumfor state-owned MNCs exists in capital-intensive industries. This finding is important, as it showsthat selection reversal exists in industries with more severe distortions in the input market.20

To verify that input distortion plays an essential role in interpreting the productivity premiumof state-owned MNCs (compared to private MNCs), we need to make sure that both SOEs andprivate firms have similar productivity dispersions (also implied by our model in the next section).Admittedly, the productivity distribution of SOEs might have a different level of dispersioncompared with that of private firms, and the productivity distribution may change during theera of SOE reforms (see, e.g., Lardy, 2004; Hsieh and Song, 2015). However, we show that theproductivity distribution of state-owned MNCs first-order stochastically dominates that of privateMNCs in Online Appendix A (i.e., state-owned MNCs are more productive than private MNCsat each percentile of the distribution).

Finally, as all of the TFP estimates are essentially based on the Olley–Pakes approach, whichuses investment as a proxy for TFP. A possible concern with the Olley–Pakes approach that usesinvestment as a proxy in the first stage is that investment in developing countries like China islumpy and the existence of too many zeros can create bias. However, this is not a problem inour estimations as discussed in Feenstra et al. (2014). In particular, we have already droppedthose bizarre observations in our sample following the General Accepted Accounting Principlecriteria. Also, the most recent advances on TFP estimation and identification such as Ghandi etal. (2016) suggest that adopting the gross output production functions can better mitigate thispotential problem. Therefore, all TFP estimates in the present article adopt the approach of thegross output production function. Still, for the sake of completeness, we report simple labourproductivity (defined as value-added per employee) and Levinsohn-Petrin (2003) TFP in OnlineAppendix Table 3. Once again, we see that state-owned non-MNCs are less productive thanprivate non-MNCs. But the opposite is true for MNCs: State-owned MNCs are more productivethan private MNCs. In short, our first empirical finding is robust.

2.3.2. Stylised fact two: Smaller fraction of state-owned MNCsColumns (3) and (6) of Table 3 present our second stylised fact. That is, the fraction of MNCs islarger among private firms than among SOEs. Again, this finding is robust to different definitionswe use to construct the SOE indicator and the different time periods we focus on. When usinga broadly defined SOE indicator, we find that more firms are classified as SOEs whereas thenumber of state-owned MNCs does not change much for the sample of 2000–8. For the periodof 2000–13, the share of MNCs increases both among SOEs and among private firms compared

19 In particular, among the 28 CIC two-digit industries, the following industries are classified as labour-intensivesectors: processing of foods (code: 13), manufacture of foods (14), beverages (15), textiles (17), apparel (18), leather(19) and timber (20).

20 Section 4 shows that the input price wedge mainly exists in the credit (i.e., capital) market.


13

Table 5. Relative Size Premium for SOEs.Year coverage Avg. ≤ 2001 ≤ 2002 ≤ 2003 ≤ 2004 ≤ 2005 ≤ 2006 ≤ 2007 ≤ 2008

Relative size of MNCs to non-exporting firms (lo/ld)(1) Private firms 4.50 4.59 4.59 4.56 4.54 4.53 4.52 4.51 4.50(2) SOE 5.48 5.65 5.64 5.58 5.55 5.53 5.51 5.49 5.48Size difference =(1) − (2)

− 0.97*** − 1.06*** − 1.05*** − 1.02*** − 1.01*** − 1.00*** − 0.99*** − 0.98*** − 0.98***

(−488.1) (−234.0) (−283.5) (−329.0) (−374.1) (−400.1) (−430.4) (−445.5) (−466.6)

Notes: This table reports the difference in relative firm size between private MNCs and state-owned MNCs. Firm size is measured by log employment.The table shows that the relative size of FDI firms to non-exporting firms is smaller for private firms than that for SOEs. This finding is consistentwith part 1 of Proposition 3, that relative size of MNCs is smaller for private firms than for SOEs. The numbers in parentheses are t-values. *** (**,*) denotes significance at the 1% (5%, 10%) level.

with the period of 2000–8. In all four cases (two time periods and two definitions of SOEs), thereare always disproportionately more MNCs among private firms than among SOEs. On the onehand, this finding is puzzling, since SOEs are larger firms that should be more likely to investabroad. Furthermore, the Chinese government has supported its SOEs’ investing abroad for manyyears, known as the Going-Out strategy. On the other hand, such an observation is consistentwith our first finding. Namely, as state-owned MNCs are more productive than private MNCs,the fraction of SOEs engaged in FDI should be smaller (i.e., tougher selection).

2.3.3. Stylised fact three: Larger relative size premium for state-owned MNCsOur last stylised fact is related to the relative size premium of state-owned MNCs. The conven-tional view is that SOEs are larger in size, which is usually measured by log employment or logsales. Our data also exhibit such features, as shown in Table 4 of the Online Appendix.

More importantly, the size premium for state-owned MNCs holds in the relative sense as well.Table 5 shows that the ratio of average log employment of multinational parent firms to that ofnon-exporting firms is larger among SOEs than among private firms. Table 5 reports the resultobtained from the comparison between the relative size of state-owned MNCs and that of privateMNCs. The relative size is measured by ljo / l

j

d , where ljo and l

j

d are the average log employmentof MNCs and that of non-exporting firms for firm type j (i.e., private or state-owned). The year-average ratio in the first column shows that the relative size of private MNCs is significantlysmaller than that of SOEs. As few SOEs were engaged in outward FDI before 2005, we reportthe year-average ratio up to a particular year in Table 5 as well. All columns suggest largerrelative size for state-owned MNCs. To sum up, our third stylised fact states that the absoluteand relative sizes of private MNCs (compared with non-exporting firms) are smaller than thoseof state-owned MNCs.

Thus far, we have established three interesting empirical findings. In what follows, we willpresent a model to rationalise these findings. Furthermore, the model yields several additionalempirical predictions, which will be shown to be consistent with the data.

3. Model

We modify the standard horizontal FDI model proposed by HMY (2004) to rationalise theempirical findings documented so far. We study how discrimination against private firms in theinput market affects the sorting pattern of MNCs and their size premium at the intensive margin.



At the same time, we investigate how the difference in foreign investment costs impacts theinvestment behaviour of private MNCs and state-owned MNCs at the extensive margin.21

3.1. Setup

There is one industry populated by firms that produce differentiated products under conditionsof monopolistic competition à la Dixit and Stiglitz (1977). Each variety is indexed by ω, and �is the set of all varieties. Consumers derive utility from consuming these differentiated goodsaccording to

U =[ ∫

ω∈�q(ω)

σ − 1σ dω

] σσ − 1 , (1)

where q(ω) is the consumption of variety ω, and σ is the constant elasticity of substitutionbetween differentiated goods.

Entrepreneurs can enter the industry by paying a fixed cost, fe, in terms of the unit of goodsproduced by the firm.22 After paying the entry cost, the entrepreneur receives a random draw ofproductivity, ϕ, for her firm. The cumulative density function of this draw is assumed to be F(ϕ).Once the entrepreneur observes the productivity draw, she decides whether or not to stay in themarket as there is a fixed cost to produce, fD, (in terms of the units of the goods produced by thefirm).

After entering and choosing to stay in the domestic market, each entrepreneur also chooseswhether to serve the foreign market. There are two options for doing this, the first of which isexporting. Exporting entails a variable trade cost, τ (≥ 1), and a fixed exporting cost, fX. Thesecond way is to set up a plant in the foreign country and produce there directly. The cost ofdoing this is fixed and denoted by fI. Both fixed costs of serving the foreign market are in termsof the units of the goods produced by the firm.

Similar to Bernard et al. (2007), there are two factors of production, capital (or land) andlabour, and the production function takes the following constant-elasticity-of-substitution form:

q(k, l) = ϕ(k

μ − 1μ + l

μ − 1μ

) μμ − 1 , (2)

where k and l are capital (or land) and labour inputs respectively, and ϕ is the productivity drawthe firm receives. Parameter μ( ≥ 1) is the elasticity of substitution between capital and labour.

We assume that there are two types of firms in the economy: private firms and SOEs.23 Thekey innovation of the model is to introduce a wedge between the input price paid by SOEs andby private enterprises when they produce domestically. Specifically, it is assumed that privatefirms pay a capital rental price (or the unit land price) c( > 1) times as high as what SOEs pay

21 Major predictions of the canonical horizontal FDI model a la HMY (2004) are consistent with our empirical findingsdocumented in Table 2. For instance, average productivity of MNCs is higher than that of non-multinational firms (seecolumns 3 and 5 of the table). Moreover, after the propensity score matching, we find that average productivity ofnon-multinational firms (domestic firms plus exporting but non-multinational firms) is higher than that of domestic firms(see columns 2 and 4 of the table).

22 We follow Bernard et al. (2007) to choose this specification in order to make various fixed costs have the samecapital (or land) intensity as the variable cost.

23 We do not take a stance on why some firms become SOEs (or private enterprises), since the predictions of the modeldo not depend on this.


15

when they produce domestically. However, firms pay the same wage and capital rental price (orthe unit land price) when producing abroad.24

Based on equation (2), we derive total variable cost and total fixed cost as

T V C(q, ϕ) = qr

ϕ(1 + ωμ−1)1

μ − 1,

and

FC(r, w) = fir

(1 + ωμ−1)1

μ − 1,

where r and w are the capital rental price (or the unit land price) and the wage rate and i ∈ {D, X,I}. Variable ω = r

wis relative price of capital (or land). Capital (or land) intensity in equilibrium

is given by l(w,r)wk(w,r)r = ωμ−1. As long as μ > 1, a higher relative price of capital leads to lower

capital (or land) intensity. This property is utilised in our productivity estimation.

3.2. Domestic Production, Exporting and FDI

We derive firm profit and revenue as follows. Based on equation (1), the demand function forvariety ω can be derived as

q(ω) = p(ω)−σ

P 1−σE,

where E is the total income of the economy and P is the ideal price index and defined as

P ≡[∫

�(ω)∈�p1−σ (ω)MdF (ω)

] 11 − σ

,

where M is the total mass of varieties in equilibrium. The resulting revenue function is

R(q) = qσ − 1

σ E

1

σ P β, β ≡ σ − 1σ

.

We derive the SOE’s operating profit of domestic production and exporting first. Since bothtypes of production use domestic factors only, their operating profits are given by

πSD(ϕ) = DHσ

(βϕrH

)σ−1(1 + ωμ−1H )

σ − 1μ − 1 ,

and

πSX(ϕ) = πSD(ϕ) + DFσ

( βϕτrH

)σ−1(1 + ωμ−1H )

σ − 1μ − 1 ,

24 We will show that there is evidence for the existence of an input price wedge in the credit land markets, but not inthe labour market. Since buying capital usually requires a substantial amount of borrowing, we assume that private firmspay a higher capital rental price than SOEs.



where Di ≡ P σ−1i Ei and i ∈ {H, F}. Subscripts S , D, X, H and F refer to SOE, domesticproduction, exporting, home country and foreign country, respectively. For private firms, theoperating profits are

πPD(ϕ) = DHσ

( βϕcrH

)σ−1(1 + (cωH )μ−1)

σ − 1μ − 1 ,

and

πPX(ϕ) = πPD(ϕ) + DFσ

( βϕτcrH

)σ−1(1 + (cωH )μ−1)

σ − 1μ − 1 .

Firm’s revenue is Rij(ϕ) = σπ ij(ϕ) where i ∈ {S, P} and j ∈ {D, X}.We can derive the exit cutoff and the exporting cutoff for SOEs and private firms respectively:

ϕ̄SD = rH (σrHfD/DH )1

σ − 1

β(

1 + ωμ−1H) σ

(σ − 1))μ − 1); ϕ̄SX = τ rH (σrHfX/DF )

1

σ − 1

β(

1 + ωμ−1H) σ

(σ − 1))μ − 1);

ϕ̄PD = crH (σcrHfD/DH )1

σ − 1

β(

1 + (cωH )μ−1) σ

(σ − 1))μ − 1); ϕ̄PX = τ crH (σcrHfX/DF )

1

σ − 1

β(

1 + (cωH )μ−1) σ

(σ − 1))μ − 1).

Note that ϕ̄PD > ϕ̄SD andϕ̄PX

ϕ̄PD= ϕ̄SX

ϕ̄SD.

Now, we discuss the case of FDI. Following HMY, we assume that the firm uses foreign factorsto produce after setting up a plant in the foreign country.25 In addition, foreign factors are usedto pay for the fixed FDI cost.26 Thus, the operating profit of firms that engage in FDI is:

πSO (ϕ) = πSD(ϕ) + DFσ

(βϕrF

)σ−1(1 + ωμ−1F )

σ − 1μ − 1 ;

πPO(ϕ) = πPD(ϕ) + DFσ

(βϕrF

)σ−1(1 + ωμ−1F )

σ − 1μ − 1 .

When both SOEs and private firms produce abroad, they face the same factor prices. TheFDI cutoffs are pinned down by the following indifference conditions (between exporting and

25 In our data set of Chinese MNCs from Zhejiang, we checked whether firms increased their foreign investment afterthe initial investment and ended up with few cases. The finding is that at least a substantial fraction of factors used inforeign production (including capital and land) is sourced from the foreign country.

26 It is worth stressing that our theoretical predictions will hold well independent of this assumption. In OnlineAppendix D, we allow for FDI fixed cost to be paid using domestic factors, and private firms do not face discriminationwhen they pay the FDI fixed cost using domestic factors. In both cases, our theoretical results are still preserved.


17

engaging in FDI):

fI rF

(1 + ωμ−1F )1

μ − 1− fXrH

(1 + ωμ−1H )1

μ − 1= DF

σ

(βϕ̄SO

)σ−1[ (1 + ωμ−1F )σ − 1μ − 1

rσ−1F

− (1 + ωμ−1H )

σ − 1μ − 1(

τrH)σ−1

],

and

fI rF

(1 + ωμ−1F )1

μ − 1− fXcrH

(1 + (cωH )μ−1)1

μ − 1= DF

σ

(βϕ̄PO

)σ−1[ (1 + ωμ−1F )σ − 1μ − 1

rσ−1F

− (1 + (cωH )μ−1)

σ − 1μ − 1(

cτrH)σ−1

].

It is evident that selection into FDI is tougher for SOEs than for private firms (i.e., ϕ̄SO > ϕ̄PO),as the opportunity cost of engaging in FDI is smaller for private firms than for SOEs.

3.3. Domestic Distortion and Patterns of Outward FDI

In this subsection, we discuss how the existence of domestic distortions in the capital and landmarkets affects the patterns of outward FDI at the extensive and intensive margins.

PROPOSITION 1. Sorting Patterns of Private Firms and SOEs (Extensive Margin):

(1) The exit cutoff and exporting cutoff are higher for private firms than for SOEs. However,the cutoff for becoming an MNC is lower for private firms than for SOEs (i.e., selectionreversal).

(2) Conditional on the initial productivity draw (and other firm-level characteristics), privatefirms are more likely to become MNCs.

(3) Assume that the truncated distribution of the productivity draw for private firms (weakly)first order stochastically dominates (FOSD) that of SOEs, or the two conditional probabilitydensity functions (PDFs) satisfy the (weak) monotone likelihood ratio property (MLRP)with:

∂

∂ϕ

(fP (ϕ|ϕ ≥ ϕ0)fS(ϕ|ϕ ≥ ϕ0)

)≥ 0 ∀ϕ ≥ ϕ0,

where fP(ϕ|ϕ ≥ ϕ0) and fS(ϕ|ϕ ≥ ϕ0) are the truncated probability density functions of theproductivity draw for private firms and SOEs, respectively. Then, the fraction of MNCs is



larger among private firms than among SOEs. Furthermore, simple average productivity ofprivate firms is greater than that of SOEs overall.

(4) Assume that both types of firms draw productivities from the same distribution (whichtrivially satisfies weak FOSD property). Then the (simple) average productivity of privateMNCs is smaller than that of state-owned MNCs (i.e., productivity premium for state-ownedMNCs).

PROOF. See Online Appendix C. �

The intuition for Proposition 1 is as follows. First, since there is discrimination against privatefirms at home, it is more difficult for private firms to survive and export. As a result, the exitcutoff and the exporting cutoff are higher for these firms. Absent the choice of exporting, the FDIcutoff would be the same for SOEs and for private firms, as they would face the same benefitand costs of doing FDI in the relative sense. However, since the firm at the FDI cutoff comparesexporting with FDI, the (opportunity) cost of engaging in FDI is smaller for private firms thanfor SOEs.27 As a result, the FDI cutoff is lower for private firms than for SOEs. Online AppendixTable 8 shows that the selection reversal holds, as the estimated productivity at the 1% (and 5%)percentile is higher for state-owned MNCs than for private MNCs. If we make assumptions onthe distribution of the productivity draws, the selection reversal leads to an average productivitypremium for state-owned MNCs, and the above theoretical results rationalise the first two stylisedfacts.28 Finally, Table 6 and Online Appendix Tables 5–6. in the next section show the lowerprobability of becoming an MNC for SOEs.

We next discuss how a variation in the level of domestic distortion affects the sorting patternof private MNCs and state-owned MNCs differently using the following proposition.

PROPOSITION 2. Cross-Industry Variations:

(1) In industries with more severe distortion (i.e., c↑), the productivity premium of state-ownedMNCs is larger. Moreover, SOEs are less likely to produce abroad in industries with moresevere distortion than SOEs in industries with less severe distortion.

(2) Assume that the production function is Cobb-Douglas with capital and labour. Then, theproductivity premium of state-owned MNCs is more pronounced in capital intensive indus-tries. Furthermore, SOEs are less likely to engage in FDI (compared with private firms) incapital intensive industries.


The intuition for the Proposition 2 is straightforward. Since the asymmetric distortion disin-centives SOEs to produce abroad, the selection into the FDI market becomes more stringent forSOEs (than for private firms) in industries with more severe discrimination against private firms.Furthermore, as the distortion exists in the capital market, we expect a more stringent selectioninto the FDI market for SOEs (than for private firms) in capital intensive industries. We willprovide empirical evidence for these two predictions in what follows.

27 Exporting does not eliminate the distortion private firms face in the domestic market.28 The selection reversal holds irrespective of the distribution of the initial productivity draw. The average productivity

premium for state-owned MNCs exists, if SOEs and private firms draw productivity from the same distribution. However,the assumption of the same productivity distribution is not required. What we need is that a lower cutoff on the productivitydraw implies a smaller average productivity (i.e., a relationship between the marginal productivity and the inframarginalproductivity). This is why we need MLRP for part 3 of the above proposition.


19

Tabl

e6.

Pri

vate

Fir

ms

Are

Mor

eL

ikel

yto

Und

erta

keF

DI.

Reg

ress

and:

LPM

Log

itL

ogit

Rar

eev

ent

Com

plem

enta

rylo

g-lo

gFD

Iin

dica

tor

Log

itY

ear

cove

rage

:20

00–8

2004

–8

SOE

defin

ed:

Nar

row

Nar

row

Nar

row

Nar

row

Nar

row

Bro

adN

arro

wN

arro

wN

arro

wN

arro

wV

aria

ble:

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

SOE

indi

cato

r−

0.00

2**

−0.

454*

−0.

757*

**−

1.30

6***

−0.

693*

**-0

.682

***

−1.

179*

**−

0.53

2*−

0.70

3***

−0.

662*

*

(−2.

41)

(−1.

85)

(−2.

88)

(−12

.63)

(−2.

81)

(−2.

88)

(−3.

26)

(−1.

73)

(−2.

68)

(−2.

56)

Firm

TFP

0.00

9***

1.33

3*1.

716*

*4.

237*

**1.

838*

*1.

843*

*1.

552*

1.60

32.

360*

**2.

500*

**

(4.1

4)(1

.80)

(2.0

0)(1

8.50

)(2

.25)

(2.2

6)(1

.66)

(1.2

8)(4

.25)

(4.9

6)L

ogfir

mla

bour

0.00

3***

0.58

9***

0.62

3***

0.58

8***

0.58

7***

0.58

7***

0.56

5***

0.73

4***

0.57

4***

0.56

7***

(6.5

5)(1

0.85

)(9

.78)

(38.

49)

(8.8

6)(8

.86)

(7.8

6)(8

.21)

(9.3

7)(1

1.03

)E

xpor

tind

icat

or0.

004*

**0.

900*

**1.

142*

**1.

102*

**1.

145*

**1.

145*

**1.

167*

**0.

736*

**1.

145*

**1.

174*

**

(7.4

2)(4

.45)

(6.0

3)(2

6.01

)(6

.03)

(6.0

3)(5

.43)

(3.8

1)(5

.82)

(6.4

9)Y

ear

fixed

effe

cts

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

fixed

effe

cts

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fore

ign

firm

sdr

oppe

dN

oN

oY

esY

esY

esY

esY

esY

esY

esY

esTa

xha

ven

drop

ped

No

No

No

No

No

No

Yes

No

No

No

Dis

tr.FD

Idr

oppe

dN

oN

oN

oN

oN

oN

oN

oY

esN

oN

oSw

itchi

ngSO

ED

ropp

edN

oN

oN

oN

oN

oN

oN

oN

oY

esY

esM

&A

deal

sdr

oppe

dN

oN

oN

oN

oN

oN

oN

oN

oN

oY

esO

bser

vatio

ns1,

136,

603

1,13

5,46

789

5,20

989

6,31

489

5,20

989

5,21

089

4,81

589

3,75

470

1,27

770

1,20

4

Not

es:

The

regr

essa

ndis

the

FDI

indi

cato

r.A

llco

lum

nsin

clud

ein

dust

ry-s

peci

ficfix

edef

fect

san

dye

ar-s

peci

ficfix

edef

fect

s.T

henu

mbe

rsin

pare

nthe

ses

are

t-va

lues

clus

tere

dat

the

firm

leve

l.**

*(*

*)

deno

tes

sign

ifica

nce

atth

e1%

(5%

)le

vel.

Col

umns

(1)–

(2)

incl

ude

fore

ign-

inve

sted

firm

sw

here

asal

loth

erco

lum

nsdr

opth

ose

firm

s.C

olum

ns(1

)–(8

)co

ver

data

over

the

peri

odof

2000

–8w

here

asco

lum

ns(9

)–(1

0)co

ver

data

over

the

peri

odof

2004

–8.C

olum

n(6

)us

esbr

oadl

yde

fined

SOE

.Col

umn

(7)

drop

sou

twar

dFD

Ito

tax

have

nde

stin

atio

ns.C

olum

n(8

)dr

ops

dist

ribu

tion-

orie

nted

FDI

(i.e

.,D

istr.

FDI)

.Col

umn

(9)

drop

sth

esw

itchi

ngSO

Es

(i.e

.,sw

itchi

ngfr

omSO

Es

topr

ivat

efir

ms)

.Col

umn

(10)

drop

sbo

thsw

itchi

ngSO

Es

and

mer

ger

and

acqu

isiti

onde

als.

Inal

lcol

umns

,TFP

ism

easu

red

byau

gmen

ted

Olle

y–Pa

kes

cont

rolli

ngfo

rin

putp

rice

dist

ortio

nan

dSO

Est

atus

.



Tabl

e7.

Dis

tort

ions

inIn

putF

acto

rsM

arke

ts.

Reg

ress

and

Mea

sure

dfir

min

tere

stra

tes

Firm

-lev

elun

itla

ndpr

ice

City

-lev

elun

itla

ndpr

ice

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

SOE

indi

cato

r−0

.134

*−0

.174

***

-0.0

89**

−9.3

9***

−6.7

8***

(−1.

90)

(−3.

34)

(−2.

28)

(−4.

43)

(−3.

02)

One

-yea

rla

gof

SOE

indi

cato

r−1

1.43

***

(−4.

47)

SOE

inte

nsity

−54.

53*

(−1.

84)

One

-yea

rla

gof

SOE

inte

nsity

−48.

97*

(−1.

67)

Oth

erfir

mfa

ctor

sco

ntro

lsN

oY

esY

esN

oY

esY

esN

oN

oY

ear-

spec

ific

fixed

effe

cts

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Indu

stry

-spe

cific

fixed

effe

cts

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Prov

ince

-spe

cific

fixed

effe

cts

No

Yes

Yes

No

No

No

No

No

City

-spe

cific

fixed

effe

cts

No

No

No

No

No

No

Yes

Yes

Yea

rco

vera

ge20

00-0

820

00-1

320

00-0

8N

umbe

rof

Obs

.1,

119,

454

1,11

9,44

61,

136,

049

208,

320

157,

810

103,

826

1,48

91,

306

R-s

quar

ed0.

010.

010.

010.

070.

070.

080.

130.

15

Not

es:C

olum

ns(1

)–(3

)an

d(7

)–(8

)co

ver

the

peri

od20

00–8

whe

reas

colu

mns

(4)–

(6)

cove

rth

epe

riod

2000

–13.

The

regr

essa

ndin

colu

mns

(1)–

(3)

isth

efir

m-l

evel

inte

rest

rate

calc

ulat

edas

the

ratio

offir

m’s

inte

rest

expe

nses

tocu

rren

tlia

bilit

ies

inco

lum

ns(1

)an

d(2

)an

dto

tota

llia

bilit

ies

inco

lum

n(3

).T

here

gres

sand

inco

lum

ns(4

)–(6

)is

the

firm

-lev

elpr

ice

ofla

ndpu

rcha

sed

from

the

gove

rnm

ent.

Thi

sis

defin

edas

the

ratio

ofth

efir

m’s

tota

lspe

ndin

gon

land

acqu

isiti

onto

the

area

ofla

ndit

purc

hase

s.T

here

gres

sand

inco

lum

ns(7

)–(8

)is

the

pref

ectu

ral

city

-lev

elpr

ice

ofla

ndpu

rcha

sed

byfir

ms

from

the

gove

rnm

ent.

Thi

sis

defin

edas

the

ratio

ofgo

vern

men

t’sto

tal

land

reve

nue

toits

land

area

inea

chpr

efec

tura

lci

ty.

The

SOE

inte

nsity

inco

lum

ns(7

)–(8

)is

defin

edas

the

num

ber

ofSO

Es

divi

ded

byth

eto

tal

num

ber

ofm

anuf

actu

ring

firm

sw

ithin

each

pref

ectu

ral

city

.A

llco

lum

nsco

ntro

lfor

year

-spe

cific

and

indu

stry

-spe

cific

fixed

effe

cts,

resp

ectiv

ely.

Col

umns

(2)a

nd(3

)add

othe

rcon

trol

sof

firm

-lev

elch

arac

teri

stic

ssu

chas

log

firm

labo

ur,f

orei

gnin

dica

tor,

expo

rtdu

mm

y,an

dpr

ovin

ce-s

peci

ficfix

edef

fect

s.C

olum

ns(5

)–(6

)ad

dot

her

cont

rols

offir

m-l

evel

char

acte

rist

ics

such

asfir

m’s

capi

tal–

labo

urra

tio,f

orei

gnin

dica

tor

and

expo

rtdu

mm

y.C

olum

n(6

)us

esth

ela

gof

SOE

indi

cato

rw

here

asco

lum

n(8

)us

esth

ela

gof

SOE

inte

nsity

inth

ere

gres

sion

s.T

henu

mbe

rsin

pare

nthe

ses

are

t-va

lues

.***

(**,

*)

deno

tes

sign

ifica

nce

atth

e1%

(5%

,10%

)le

vel.


21

Tabl

e8.

Log

itE

stim

ates

onC

hann

els.

Mea

sure

ofin

putp

rice

Mea

sure

dfir

m-l

evel

inte

rest

rate

Firm

-lev

ella

ndpr

ice

Reg

ress

and:

FDI

indi

cato

r(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)(1

1)

SOE

indi

cato

r−

0.26

4**

−0.

488*

**−

0.99

4***

−0.

290*

*−

0.25

0**

−0.

254*

*−

0.63

8***

−1.

069*

**−

1.34

3***

−1.

850*

**−

1.85

5***

(−2.

33)

(−4.

22)

(−6.

36)

(−2.

09)

(−1.

97)

(−2.

11)

(−7.

67)

(−8.

42)

(−10

.46)

(−6.

61)

(−6.

60)

SOE

indi

cato

r×

−0.

638*

−0.

886*

*−

0.94

8*−

1.03

3**

−0.

817*

*−

0.76

7*−

0.00

3**

−0.

006*

**−

0.00

6***

−0.

006*

**−

0.00

6**

Ind.

inpu

tpri

cedi

ff.

(−1.

69)

(−2.

41)

(−1.

90)

(−2.

19)

(−2.

10)

(−1.

89)

(−2.

10)

(−4.

01)

(−4.

35)

(−2.

76)

(−2.

36)

Ind.

inpu

tpri

cedi

ff.

0.01

90.

057

0.07

9*0.

090

0.12

70.

019

0.00

10.

001*

**0.

001*

**0.

001*

*0.

001*

*

(0.5

4)(1

.56)

(1.8

4)(1

.30)

(0.8

0)(0

.53)

(1.3

5)(3

.27)

(3.4

0)(2

.34)

(2.2

4)O

ther

cont

rols

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rfix

edef

fect

sY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esIn

dust

ryfix

edef

fect

sY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esY

esFo

reig

nfir

ms

incl

uded

Yes

No

No

No

No

Yes

Yes

Yes

No

No

No

Tax

have

nin

clud

edY

esY

esN

oY

esY

esY

esY

esN

oN

oN

oN

oD

istr

ibut

ion

FDI

incl

uded

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rco

vera

ge20

00−0

820

04−0

820

00-1

320

00−0

820

04−0

8O

bser

vatio

ns1,

121,

845

879,

003

873,

150

829,

655

832,

741

883,

712

2,27

8,06

22,

200,

723

1,75

0,93

91,

005,

294

739,

082

Not

es:T

here

gres

sand

isth

eFD

Iind

icat

or.T

henu

mbe

rsin

pare

nthe

ses

are

t-va

lues

.***

(**)d

enot

essi

gnifi

canc

eat

the

1%(5

%)l

evel

.Inp

utpr

ices

inco

lum

ns(1

)–(6

)are

mea

sure

dby

firm

-lev

elin

tere

stra

tew

here

asth

ose

inco

lum

ns(7

)–(1

1)ar

efir

m-l

evel

unit

land

pric

e.T

hem

easu

red

inte

rest

rate

isca

lcul

ated

asth

era

tioof

firm

’sin

tere

stex

pens

esto

curr

ent

liabi

litie

sin

colu

mns

(1)–

(4)

and

(6)

and

toto

tal

liabi

litie

sin

colu

mn

(5).

Inpa

rtic

ular

,th

ein

dust

ryin

tere

stra

te(o

run

itla

ndpr

ice)

diff

eren

tial

(i.e

.,In

d.in

put

pric

edi

ff.)

ism

easu

red

byth

eav

erag

ein

dust

ry-l

evel

inte

rest

rate

(uni

tlan

dpr

ice)

paid

bypr

ivat

efir

ms

min

usth

atpa

idby

SOE

sin

each

thre

e-di

gitC

ICin

dust

ries

byye

ar.C

olum

ns(1

)–(5

)an

d(1

0)co

verd

ata

over

the

peri

od20

00–8

whe

reas

colu

mns

(6)a

nd(1

1)co

verd

ata

over

the

peri

od20

04–8

.Col

umns

(7)–

(9)c

over

data

over

the

peri

od20

00–1

3.C

olum

ns(2

)–(5

)and

(9)–

(11)

drop

fore

ign

firm

s.C

olum

ns(3

)an

d(8

)–(1

1)dr

opFD

Ito

tax

have

nde

stin

atio

nco

untr

ies.

Col

umns

(4)

and

(5)

drop

dist

ribu

tion

FDI.

All

colu

mns

incl

ude

othe

rfir

m-l

evel

cont

rols

such

asfir

mT

FP(i

nco

lum

ns(1

)–(6

)on

ly),

log

empl

oym

ent

and

expo

rtin

dica

tor.

All

regr

essi

ons

incl

ude

indu

stry

-spe

cific

fixed

-eff

ects

and

year

-spe

cific

fixed

-eff

ects

whe

reas

colu

mn

(11)

even

cont

rols

the

indu

stry

-yea

rsp

ecifi

cfix

ed-e

ffec

ts.



Table 9. Logit Estimates by Sectors.

Sectoral category: 2000–8 2004–8

Regressand: FDI indicator (1) (2) (3) (4) (5)

SOE indicator × − 0.276 − 0.290 − 0.690 − 0.180 − 0.170Labor-intensive indicator ( − 0.68) ( − 0.70) ( − 1.36) ( − 0.44) ( − 0.35)SOE indicator × − 0.475* − 0.754*** − 1.257*** − 0.834*** − 0.646**Capital-intensive indicator ( − 1.73) ( − 2.70) ( − 2.98) ( − 3.09) ( − 2.35)Firm relative TFP 1.305* 1.837** 1.551* 2.328*** 2.069***

(1.81) (2.25) (1.66) (4.20) (3.82)Log firm labour 0.582*** 0.587*** 0.565*** 0.570*** 0.539***

(11.18) (8.84) (7.85) (9.61) (19.46)Export indicator 0.896*** 1.146*** 1.167*** 1.152*** 1.297***

(4.44) (6.03) (5.43) (5.93) (18.71)Year fixed effects Yes Yes Yes Yes YesIndustry fixed effects Yes Yes Yes Yes YesForeign firms dropped No Yes Yes Yes YesTax haven destinations dropped No No Yes No NoSOE switching firms dropped No No No No YesObservations 1,135,468 895,210 894,816 707,154 554,768

Notes: The regressand is the FDI indicator. All columns include industry-specific fixed effects and year-specific fixedeffects. The numbers in parentheses are t-values clustered at the firm level. *** (**) denotes significance at the 1%(5%) level. Columns (1)–(3) cover observations during the years 2000–8, whereas columns (4)–(5) cover observationsduring the years 2004–8. Column (1) keeps foreign invested firms whereas the other columns drop foreign invested firms.Column (3) drops outward FDI to tax-haven regions. Column (5) drops SOE switching firms. The relative TFP in columns(1)–(5) are measured by augmented Olley–Pakes controlling for input price distortion and SOE status labour-intensivesectors indicator equals one if the firm’s Chinese industrial classification is higher than 20 and zero otherwise.

Finally, we discuss how domestic distortion affects the sorting patterns of MNCs at the intensivemargin.

PROPOSITION 3. Sorting Pattern of Private Firms and SOEs (Intensive Margin):

(1) Suppose the initial productivity draw follows a Pareto distribution with the same shapeparameter for private firms and SOEs. Then, the relative size of private MNCs in thedomestic market (i.e., compared with private non-exporting firms) is smaller than that ofstate-owned MNCs (i.e., compared with non-exporting SOEs).

(2) Conditional on productivity and other firm-level characteristics, the ratio of foreign salesto domestic sales is higher for private MNCs than for state-owned MNCs.


The intuition for Proposition 3 is straightforward. Since there is an extra benefit for privatefirms to produce abroad, they produce and sell more in the foreign market. This effect is anotherkey result of our model, for which we provide empirical support in the next section. The first partof Proposition 3 receives strong statistical support from Table 5. As t

outward fdi and domestic input distortions: evidence …mjyu.ccer.pku.edu.cn/docs/ej_cty.pdf · *...

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